Spatial Transcriptomics for Winter School
Sami
1/20/2021
#Spatial Transcriptomics – workshop for Visium data analysis Welcome to the Spatial Transcriptomics workshop! It is based on a previous workshop of Alma Andersson, the same one who developed the single-cell and spatial data integration tool, Stereoscope and the materials from the publication of Bergenstråhle et al. (2020) and https://ludvigla.github.io/STUtility_web_site/.
#Introduction We will explore the field of Spatial Transcriptomics. Many of the ideas that you encountered when working with single cell data will be revistied but now with the advantage that we can visualize our results in the real physical space (not just UMAP or tSNE space).
The field of spatial transcriptomics has grown rapidly, and multiple techniques to obtain information of spatial gene expression exists. Some examples are : MERFISH, ISS, osmFISH, seqFISH, HDST, baristaSeq, Slide-seq, and GeoMx. We will however be focusing on spatial data generated from the Visium platform (sold by 10x Genomics). Visium is the successor to the technique - somewhat confusingly - named Spatial Transcriptomics (commonly referred to as ST).
ST was developed at SciLifeLab and presented to the world in 2016 when the publication Visualization and analysis of gene expression in tissue sections by spatial transcriptomics was published in the journal Science. In December2018, 10x Genomics aquired the IP rights to the ST-technique, they then launched the Visium platform in late 2019.
Both ST and Visium utilize a solid array onto which oligonucleotides with spatial barcodes have been printed at locations (spots) arranged in a regular grid. These oligonucleotides all have a poly-T sequence, making them apt to capture mRNA’s by their poly-A tail. By reverse transcription, the barcodes of the oligonucleotides will be embedded in cDNA synthesized from the captured mRNA’s; hence, we know at which mRNA that each transcript was captured. Once the cDNA molecules are sequenced, we can backmap them to their spatial position - using the barcodes - and by doing so obtain spatial gene expression information. The “old” ST arrays had 1000 spots printed on the array, while the newer Visium slides has 5000 spots.
One key fact that should be emphasized is how none of these methods (ST and Visium) operate on a single cell level yet. The gene expression data associated with each spot is really a mixture of contributions from multiple cells, not all necessarliy of the same cell type.
##Install packages
To analyse the gene count matrices, we will use a package called STUtility. It was published 2020 by Bergenstråhle et al. STUtility is based on Seurat, single-cell analysis tool, of which most of you probably have heard before. In addition, STUtility depends on plethora of other packages, so installing all the packages from the scratch will take some time. This code below checks that all the packages are installed and should install the ones missing.
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## plot.imlistFirst things first: let’s discuss about the data. For this tutorial we will use publicly available data from 10x Genomics Inc. We study two sagittal sections from mouse brain. WinterSchool2021 we have several different files. We start with raw data. Raw data is unfiltered and contains the data from all the spots, also outside of the tissue. It includes also all the mitochondrial genes but let’s get back to that a bit later.
First, let’s creat a Seurat object. For that we need .h5 files which are compact gene count matrices, spotfiles which contain the positions of the tissue, imgs as bright field images from the tissue and json files as the spot coordinates. In addition, we can add sample specific metadata, like section number or slide number.
We load all this to the object using InputFromTable command:
#Lateral diffusion
#Collect file paths and metadata
samples <- list.files(path = "~/Documents/GitHub/WinterSchool2021", pattern = "raw_feature_bc_matrix.h5", recursive = TRUE, full.names = T)
spotfiles <- list.files(path = "~/Documents/GitHub/WinterSchool2021", pattern = ".csv", full.names = T)
imgs <- list.files(path = "~/Documents/GitHub/WinterSchool2021", pattern = "hires_image.png", recursive = T,full.names = T)
json <- list.files(path = "~/Documents/GitHub/WinterSchool2021", pattern = "json.json", full.names = T)
section <- c("1", "2")
slide <- c("V19L29-035", "V19L29-035")
#Create table for the metadata
infoTable <- data.frame(samples, imgs,spotfiles,json, section, slide, stringsAsFactors = F)
#Load the data based on infoTable
se <- InputFromTable(infotable = infoTable,
disable.subset = T)## Loading /Users/sami.saarenpaa/Documents/GitHub/WinterSchool2021/V1_Mouse_Brain_Sagittal_Posterior_raw_feature_bc_matrix.h5 count matrix from a 'Visium' experiment
## Loading /Users/sami.saarenpaa/Documents/GitHub/WinterSchool2021/V1_Mouse_Brain_Sagittal_Posterior_Section_2_raw_feature_bc_matrix.h5 count matrix from a 'Visium' experiment
##
## ------------- Filtering (not including images based filtering) --------------
## Spots removed: 0
## Genes removed: 9935
## Saving capture area ranges to Staffli object
## After filtering the dimensions of the experiment is: [22350 genes, 9984 spots]We can filter out some spots and genes are not meaningfull for the analysis. Commonly these genes have too low expression or they are expressed in only couple of spots. But how to choose what is good amount filtering?
Try yourself and use ST.Featureplots and Vlnplots to visualise what happens in every situation.
ST.FeaturePlot(se, features = c("nFeature_RNA"), dark.theme = T, cols = c("black", "darkblue", "cyan", "yellow", "red", "darkred"))
ST.FeaturePlot(se, features = c("nCount_RNA"), dark.theme = T, cols = c("black", "darkblue", "cyan", "yellow", "red", "darkred"))
Next, let’s look at the ribosomal and mitochondrial gene expression patterns on tissue level. We will
mt.genes <- grep(pattern = "^mt-", x = rownames(se), value = TRUE)
se$percent.mito <- (Matrix::colSums(se@assays$RNA@counts[mt.genes, ])/Matrix::colSums(se@assays$RNA@counts))*100
rp.genes <- grep(pattern = "^Rpl|^Rps", x = rownames(se), value = TRUE)
se$percent.ribo <- (Matrix::colSums(se@assays$RNA@counts[rp.genes, ])/Matrix::colSums(se@assays$RNA@counts))*100
ST.FeaturePlot(se, features = "percent.mito", dark.theme = TRUE, cols = c("dark blue", "cyan", "yellow", "red", "dark red"))
ST.FeaturePlot(se, features = "percent.ribo", dark.theme = TRUE, cols = c("dark blue", "cyan", "yellow", "red", "dark red"))
VlnPlot(se, features = c("nFeature_RNA", "nCount_RNA", "percent.mito", "percent.ribo"), ncol = 4) 
#Spots under the tissue
#Load the data based on infoTable
se.tissue <- InputFromTable(infotable = infoTable)## Using spotfiles to remove spots outside of tissue
## Loading /Users/sami.saarenpaa/Documents/GitHub/WinterSchool2021/V1_Mouse_Brain_Sagittal_Posterior_raw_feature_bc_matrix.h5 count matrix from a 'Visium' experiment
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## ------------- Filtering (not including images based filtering) --------------
## Spots removed: 0
## Genes removed: 10016
## Saving capture area ranges to Staffli object
## After filtering the dimensions of the experiment is: [22269 genes, 6644 spots]#Load images to the object
system.time({se.tissue <- LoadImages(object = se.tissue, verbose = T)})## Loading images for 2 samples:
## Reading /Users/sami.saarenpaa/Documents/GitHub/WinterSchool2021/A1_tissue_hires_image.png for sample 1 ...
## Scaling down sample 1 image from 2000x1998 pixels to 400x400 pixels
## Reading /Users/sami.saarenpaa/Documents/GitHub/WinterSchool2021/B1_tissue_hires_image.png for sample 1 ...
## Scaling down sample 2 image from 2000x1998 pixels to 400x400 pixels## user system elapsed
## 2.467 0.380 12.900We can visualise the distribution of the unique molecules and unique genes to help with the filtering.
p1 <- ggplot() +
geom_histogram(data = se.tissue[[]], aes(nFeature_RNA), fill = "brown", alpha = 0.7, color = "gray", bins = 50) +
ggtitle("Unique genes per spot")
p2 <- ggplot() +
geom_histogram(data = se.tissue[[]], aes(nCount_RNA), fill = "brown", alpha = 0.7, color = "gray", bins = 50) +
ggtitle("Total counts per spots")
gene_attr <- data.frame(nUMI = Matrix::rowSums(se.tissue@assays$RNA@counts),
nSpots = Matrix::rowSums(se.tissue@assays$RNA@counts > 0))
p3 <- ggplot() +
geom_histogram(data = gene_attr, aes(nUMI), fill = "brown", alpha = 0.7, color = "gray", bins = 50) +
scale_x_log10() +
ggtitle("Total counts per gene (log10 scale)")
p4 <- ggplot() +
geom_histogram(data = gene_attr, aes(nSpots), fill = "brown", alpha = 0.7, color = "gray", bins = 50) +
ggtitle("Total spots per gene")
cowplot::plot_grid(p1, p2, p3, p4)
mt.genes <- grep(pattern = "^mt-", x = rownames(se.tissue), value = TRUE)
se.tissue$percent.mito <- (Matrix::colSums(se.tissue@assays$RNA@counts[mt.genes, ])/Matrix::colSums(se.tissue@assays$RNA@counts))*100
rp.genes <- grep(pattern = "^Rpl|^Rps", x = rownames(se.tissue), value = TRUE)
se.tissue$percent.ribo <- (Matrix::colSums(se.tissue@assays$RNA@counts[rp.genes, ])/Matrix::colSums(se.tissue@assays$RNA@counts))*100
ST.FeaturePlot(se.tissue, features = "percent.mito", dark.theme = TRUE, cols = c("dark blue", "cyan", "yellow", "red", "dark red"))
ST.FeaturePlot(se.tissue, features = "percent.ribo", dark.theme = TRUE, cols = c("dark blue", "cyan", "yellow", "red", "dark red"))
VlnPlot(se.tissue, features = c("nFeature_RNA", "nCount_RNA", "percent.mito", "percent.ribo"), ncol = 4, pt.size = 0.05) 
#Filtering
se.subset <- SubsetSTData(se.tissue, expression = nFeature_RNA > 500 & percent.mito < 30)
ST.FeaturePlot(se.subset, features = c("nFeature_RNA"), dark.theme = T, cols = c("black", "darkblue", "cyan", "yellow", "red", "darkred"))
ST.FeaturePlot(se.subset, features = c("nCount_RNA"), dark.theme = T, cols = c("black", "darkblue", "cyan", "yellow", "red", "darkred"))
#Keeping only protein-coding genes
ensids <- read.table(file = "mouse_genes.tsv", header = T, sep = "\t", stringsAsFactors = F)
# Print available biotypes
unique(ensids$gene_type)## [1] "TEC" "snRNA"
## [3] "protein_coding" "processed_pseudogene"
## [5] "antisense" "sense_intronic"
## [7] "lincRNA" "processed_transcript"
## [9] "miRNA" "snoRNA"
## [11] "misc_RNA" "transcribed_unprocessed_pseudogene"
## [13] "unprocessed_pseudogene" "unitary_pseudogene"
## [15] "sense_overlapping" "rRNA"
## [17] "transcribed_processed_pseudogene" "ribozyme"
## [19] "scaRNA" "pseudogene"
## [21] "polymorphic_pseudogene" "macro_lncRNA"
## [23] "bidirectional_promoter_lncRNA" "3prime_overlapping_ncRNA"
## [25] "transcribed_unitary_pseudogene" "TR_V_gene"
## [27] "TR_V_pseudogene" "TR_D_gene"
## [29] "TR_J_gene" "TR_C_gene"
## [31] "TR_J_pseudogene" "IG_LV_gene"
## [33] "IG_V_gene" "IG_V_pseudogene"
## [35] "IG_J_gene" "IG_C_gene"
## [37] "sRNA" "scRNA"
## [39] "IG_C_pseudogene" "IG_D_gene"
## [41] "IG_D_pseudogene" "IG_pseudogene"
## [43] "Mt_tRNA" "Mt_rRNA"keep.genes <- subset(ensids, gene_type %in% "protein_coding")$gene_name
# Subset Seurat object
se.subset <- se.subset[intersect(rownames(se.subset), keep.genes), ]
ST.FeaturePlot(se.subset, features = c("nFeature_RNA"), dark.theme = T, cols = c("black", "darkblue", "cyan", "yellow", "red", "darkred"))
ST.FeaturePlot(se.subset, features = c("nCount_RNA"), dark.theme = T, cols = c("black", "darkblue", "cyan", "yellow", "red", "darkred"))
#Image processing
#Load images to the object
system.time({se.subset <- LoadImages(object = se.subset, verbose = T)})## Loading images for 2 samples:
## Reading /Users/sami.saarenpaa/Documents/GitHub/WinterSchool2021/A1_tissue_hires_image.png for sample 1 ...
## Scaling down sample 1 image from 2000x1998 pixels to 400x400 pixels
## Reading /Users/sami.saarenpaa/Documents/GitHub/WinterSchool2021/B1_tissue_hires_image.png for sample 1 ...
## Scaling down sample 2 image from 2000x1998 pixels to 400x400 pixels## user system elapsed
## 2.437 0.318 12.768ImagePlot(se.subset, method = "raster", darken = TRUE, type = "raw")
ST.FeaturePlot(object = se.subset,
features = c("nFeature_RNA"),
cols = c("dark blue", "cyan", "yellow", "red", "dark red"),
ncol = 2,
grid.ncol = 1,
dark.theme = T)
FeatureOverlay(se.subset,
features = "Cck",
pt.size = 1.5,
cols = c("dark blue", "cyan", "yellow", "red", "dark red"),
dark.theme = T,
type = "raw")
#Masking and alignment
se.subset <- se.subset %>% MaskImages(channels.use = 2) %>% AlignImages()
ImagePlot(se.subset, ncols = 2, method = "raster", type = "masked", darken = T)
ImagePlot(se.subset, method = "raster", darken = TRUE, type = "processed", ncols = 2)
##Normalization
We normalize our data using SCTransformm here but Harmony is another normalisation technique that is widely used in the lab. SCtransform is an alternative to NormalizeData, FindVariableFeatures and ScaleData. What do you see here? Output format: Results in new SCT assay, counts corrected, log1p(counts) as data.
Possible batch effects check:- Batch effects can bring unexplanable or even biased biological variations in the data. - Arise from multiple reasons, like different samples, storage times, different slides or different arrays within the same slide - vars.to.regress option from the SCTransform removes these batch effects. We can state which batches to choose in this option. Make sure this ‘batch’ information is available in the meta.data before proceeding.
#Default normalisation without any batch effects correction
se.subset <- SCTransform(se.subset)## Calculating cell attributes from input UMI matrix: log_umi## Variance stabilizing transformation of count matrix of size 15885 by 6554## Model formula is y ~ log_umi## Get Negative Binomial regression parameters per gene## Using 2000 genes, 6554 cells##
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|
|================== | 25%## Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached
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|
|==================================================== | 75%## Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached
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|======================================================================| 100%## Found 102 outliers - those will be ignored in fitting/regularization step## Second step: Get residuals using fitted parameters for 15885 genes##
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|======================================================================| 100%## Computing corrected count matrix for 15885 genes##
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|======================================================================| 100%## Calculating gene attributes## Wall clock passed: Time difference of 1.3586 mins## Determine variable features## Set 3000 variable features## Place corrected count matrix in counts slot## Centering data matrix## Set default assay to SCT#SCTransform with batch effect correction using 'section' and 'nFeature_RNA'
se.batch.section.cor <- SCTransform(se.subset, vars.to.regress = c("section", "nFeature_RNA"))## Calculating cell attributes from input UMI matrix: log_umi## Variance stabilizing transformation of count matrix of size 15885 by 6554## Model formula is y ~ log_umi## Get Negative Binomial regression parameters per gene## Using 2000 genes, 6554 cells##
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|
|================== | 25%## Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached
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|
|=================================== | 50%## Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached
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|
|==================================================== | 75%## Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached
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|======================================================================| 100%## Found 102 outliers - those will be ignored in fitting/regularization step## Second step: Get residuals using fitted parameters for 15885 genes##
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|======================================================================| 100%## Calculating gene attributes## Wall clock passed: Time difference of 1.345095 mins## Determine variable features## Set 3000 variable features## Place corrected count matrix in counts slot## Regressing out section, nFeature_RNA## Centering data matrix## Set default assay to SCT##Factor Analysis and Dimensionality reduction techniques
We can use factor analysis along with a variety of reduction techniques to fish out patterns of transcriptomic profiles.
Try with different combinations of nfactors and RUNXXX and see what happens. nfactors = c(20,30) RUNXXX <- PCA, TSNE, ICA, UMAP
se.subset <- RunNMF(se.subset, nfactors = 10)
cscale <- c("darkblue", "cyan", "yellow", "red", "darkred")
#Plot variable number of dimensions across the samples
ST.DimPlot(se.subset,
dims = 1:10,
ncol = 2, # dimension level column setting
grid.ncol = 2, # Sample level column setting
reduction = "NMF",
pt.size = 1,
center.zero = F,
cols = cscale)
#Print summary of most contributing genes
print(se.subset[["NMF"]])## factor_ 1
## Positive: Nrgn, Camk2n1, Calm1, Tspan7, 1110008P14Rik, Cx3cl1, Fbxl16, Syt1, mt-Co1, Stmn1
## Pcsk1n, Zfp365, Arpp19, Vamp2, Calm2, Pld3, mt-Nd2, Mef2c, Basp1, Ctxn1
## Negative: Pik3c2g, Cd109, Tspan18, Stoml3, Zfp474, Gabre, Slco1a5, Slc43a3, Prr29, Rd3
## Fxyd3, Daw1, Ackr4, Serpina3n, Ttc29, Tmem45b, Ptpro, Pcdh20, Gpr179, Tex14
## factor_ 2
## Positive: Prkcd, Tnnt1, Pcp4, Plekhg1, Tcf7l2, Pdp1, Synpo2, Adarb1, Ndrg4, Amotl1
## Rora, Ntng1, Rasgrp1, Ptpn4, Ramp3, Uchl1, Shox2, Ccdc136, Nsmf, Cplx1
## Negative: Klhl1, Cidea, Inmt, Slc38a11, Odf3, Lrrc18, Cd109, Tspan18, Prx, Sytl3
## Clic3, Catip, Acan, Stoml3, Ggt6, Ankrd34b, Gm9920, Gpr139, Agtr2, Csf2rb
## factor_ 3
## Positive: Atp6v0c, Nptxr, Ly6h, Actg1, Tubb2a, Nsg2, Rtn1, Nrgn, Calm2, Dynll1
## Caly, Calm1, Ubb, Bex2, Ywhaz, Crym, Ctxn1, Camkv, Uchl1, Thra
## Negative: Inmt, Acss3, Vnn1, Pik3c2g, Tmem252, Nr0b2, Gstt3, Tnfsf10, Angptl3, Gata6
## Satb2, Psma8, Adamtsl5, Mpl, Ear2, Arl11, Alx3, Cldn15, Six1, Pctp
## factor_ 4
## Positive: Cbln3, Cbln1, Gabra6, Nrep, Calb2, Chn2, Grin2c, Neurod1, Cnpy1, Zic1
## Grm4, Atp1a1, Adcy1, Fat2, Stmn2, Shf, Ddx5, H3f3b, Kcnc1, Pcp2
## Negative: Stoml3, Bpifb2, Slco1a5, Gm5084, Rgs18, Foxd2, Ptgfr, mt-Nd2, Nphs2, Cxcl5
## mt-Co1, Hs3st3b1, Gata3, Fgf7, Glra1, Prdm6, Tspan8, Lmx1b, Hoxb4, Rab7b
## factor_ 5
## Positive: Plp1, Mbp, Mobp, Trf, Mag, Apod, Cnp, Mal, Cldn11, Cryab
## Mog, Plekhb1, Ermn, Car2, Ugt8a, Dbndd2, Qk, Ppp1r14a, Qdpr, Ndrg1
## Negative: Vnn1, Cd200r2, Alx3, Akr1b7, Card14, En1, Foxd2, Ptgfr, Olfr922, Slc4a9
## Bnc2, AI661453, Nphs2, Hcar1, Sp5, Ccr9, Cd209g, Anxa1, Lrrc10b, Mpzl2#Barplot of the most contributing genes
FactorGeneLoadingPlot(se.subset, factor = 3, dark.theme = TRUE)
We can also view the most variable features/genes.
head(se.subset@assays$SCT@var.features, 10)## [1] "Ttr" "Hbb-bs" "Plp1" "Ptgds" "Hba-a1" "Hba-a2" "Pcp2" "Mbp"
## [9] "Car8" "Hbb-bt"most.var.genes <- se.subset@assays$SCT@var.features
fts <- c("Ttr", "Hba-a1", "Ptgds")
for (ftr in fts) {
p <- FeatureOverlay(se.subset,
features = ftr,
sampleids = 1:2,
cols = c("darkblue", "cyan", "yellow", "red", "darkred"),
pt.size = 1.5,
pt.alpha = 0.5,
dark.theme = T,
ncols.samples = 2)
print(p)
}
Below is a UMAP embedding to compare the spatial distribution of a gene. Try with t-SNE embedding
se.subset <- RunUMAP(se.subset, reduction = "NMF", dims = 1:10, n.neighbors = 10)## Warning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
## To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
## This message will be shown once per session## 22:05:57 UMAP embedding parameters a = 0.9922 b = 1.112## 22:05:57 Read 6554 rows and found 10 numeric columns## 22:05:57 Using Annoy for neighbor search, n_neighbors = 10## 22:05:57 Building Annoy index with metric = cosine, n_trees = 50## 0% 10 20 30 40 50 60 70 80 90 100%## [----|----|----|----|----|----|----|----|----|----|## **************************************************|
## 22:05:58 Writing NN index file to temp file /var/folders/6_/l891fkdx58j_sj_t2cb9xp6rs_d145/T//RtmpQTBdRp/file89a41826ef8b
## 22:05:58 Searching Annoy index using 1 thread, search_k = 1000
## 22:05:59 Annoy recall = 100%
## 22:06:00 Commencing smooth kNN distance calibration using 1 thread
## 22:06:01 Initializing from normalized Laplacian + noise
## 22:06:01 Commencing optimization for 500 epochs, with 80090 positive edges
## 22:06:07 Optimization finished#Gene list for plotting
fts <- c("Ttr", "Hba-a1", "Ptgds")
# Plot code for gene expression on visium coordinates
p3 <- ST.FeaturePlot(se.subset, features = fts, ncol = 2, grid.ncol = 1, pt.size = 1, show.sb = FALSE)
cowplot::plot_grid(p3, ncol = 2)
# Plot code for gene expression on UMAP embedding
p.fts <- lapply(fts, function(ftr) {
FeaturePlot(se.subset, features = ftr, reduction = "umap", order = TRUE)
})
cowplot::plot_grid(plotlist = p.fts, ncol = 2)
##Clustering
An important part of Genome Analysis, we will use the most ‘spatially’ contributing dimensions from factor analysis in clustering.
Try with reduction = c(PCA, UMAP)
#construct Shared Nearest Neighbor (SSN) graph
se.subset <- FindNeighbors(object = se.subset, verbose = FALSE, reduction = "NMF", dims = 1:10)
se.subset <- FindClusters(object = se.subset, verbose = FALSE)
library(RColorBrewer)
n <- 19
qual_col_pals = brewer.pal.info[brewer.pal.info$category == 'qual',]
col_vector = unlist(mapply(brewer.pal, qual_col_pals$maxcolors, rownames(qual_col_pals)))
ST.FeaturePlot(object = se.subset, features = "seurat_clusters", dark.theme = T, cols = col_vector, pt.size = 1)
If clusters seem cluttered, then view each cluster using ‘split.labels = T’ but for only one section at a time.
Try with indices = 2, for section 2/sample 2
ST.FeaturePlot(object = se.subset, features = "seurat_clusters", pt.size = 1, split.labels = T, indices = 1, show.sb = FALSE)
##Differential Expression Analysis (DEA)
We can look at the most differentially expressed genes across the tissue, also termed here as Markers.
ident.1 selects the cluster ID/number where we want to look at the differentially expressed genes/markers.
Try with other clusters as well.
diff.genes <- FindMarkers(se.subset, ident.1 = 1) #this step will take a couple of minutes to finish
head(diff.genes)## p_val avg_logFC pct.1 pct.2 p_val_adj
## Plp1 0 1.940905 1 0.907 0
## Trf 0 1.651250 1 0.826 0
## Mbp 0 1.576178 1 0.996 0
## Mobp 0 1.566703 1 0.807 0
## Mag 0 1.512568 1 0.726 0
## Mal 0 1.463331 1 0.707 0We can also look at the expression of one gene of interest across the ‘rest’ of the samples by using FeatureOverlay.
Try with other genes.
FeatureOverlay(se.subset, features = "Nrgn",
sampleids = 1:2,
cols = c("darkblue", "cyan", "yellow", "red", "darkred"),
pt.size = 1.5,
pt.alpha = 0.5,
ncols.samples = 2)
##Spatial Autocorrection
We can find genes with spatial patterns across the tissue. STUtility uses neighborhood networks to provide an overall expression of a gene relative to it neighborhood. Each gene’s orginal expression and this new relative expression is calculated. All genes are then further ranked based on this dataframe of gene expression vectors.
library(spdep)## Loading required package: sp##
## Attaching package: 'sp'## The following object is masked from 'package:IRanges':
##
## %over%## Loading required package: spData## To access larger datasets in this package, install the spDataLarge
## package with: `install.packages('spDataLarge',
## repos='https://nowosad.github.io/drat/', type='source')`## Loading required package: sf## Linking to GEOS 3.7.2, GDAL 2.4.2, PROJ 5.2.0neigh.genes <- CorSpatialGenes(se.subset)View a summary of the neigh.genes
head(neigh.genes)## gene cor
## Ttr Ttr 0.9628653
## Pcp2 Pcp2 0.9575822
## Nrgn Nrgn 0.9482509
## Mbp Mbp 0.9296262
## Camk2n1 Camk2n1 0.9293678
## Pvalb Pvalb 0.9235879